Performance evaluation of B-Tree and hash indexing under varying data sizes in relational database systems

Hassan Bediar Hashim

doi:10.59400/cai4104

Performance evaluation of B-Tree and hash indexing under varying data sizes in relational database systems

Hassan Bediar Hashim
Department of Electrical Technologies, Middle Technical University, Baghdad 10074, Iraq

Article ID: 4104

Keywords: relational database systems, B-Tree indexing, hash indexing, query response time, performance evaluation, data size

Abstract

This study investigates query performance optimization in relational database management systems (RDBMSs) by evaluating two common indexing techniques, B-Tree and Hash indexing, under varying dataset sizes. With the rapid growth of data generated by IoT systems, enterprise applications, and digital services, efficient query execution has become essential for maintaining scalability and system performance. The research compares three database configurations: no indexing, B-Tree indexing, and Hash indexing, while applying a Cost-Based Optimization (CBO) strategy to improve query plan selection. Experimental results reveal that query response time increases significantly with larger datasets, especially when no indexing is used. Both indexing methods substantially enhance performance compared to full-table scans, achieving improvements ranging from 35% to 60% depending on dataset size and query workload. The measured speedup factors reached up to 2.60×, confirming the effectiveness of indexing in reducing execution time. Further analysis indicates that B-Tree indexing consistently performs better than Hash indexing in large-scale and mixed-query environments due to its logarithmic search efficiency and support for range queries. B-Tree indexing reduced execution time to nearly 40–45% of the baseline, whereas Hash indexing achieved approximately 55–60% under similar conditions. The findings emphasize that selecting an appropriate indexing strategy is critical for optimizing database query performance, and that the effectiveness of each method depends largely on workload characteristics and dataset scale.

Published

2026-03-19

How to Cite

Hashim, H. B. (2026). Performance evaluation of B-Tree and hash indexing under varying data sizes in relational database systems. Computing and Artificial Intelligence, 4(1). https://doi.org/10.59400/cai4104

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Vol. 4 No. 1 (2026)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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